A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action-Reference-Cited by-同舟云学术

A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action

Published:2017-10-16 Issue:10 Volume:13 Page:e1005807
ISSN:1553-7358
Container-title:PLOS Computational Biology
language:en
Short-container-title:PLoS Comput Biol

Author:

Abadi Shiran^ORCID,Yan Winston X.,Amar David,Mayrose Itay^ORCID

Funder

The Ministry of Agriculture of Israel

The Rothschild Caesarea Foundation

Edmond J. Safra Center for Bioinformatics at Tel-Aviv University

The Manna Program in Food Safety and Security

The Naomi Prawer Kadar Foundation through the Tel Aviv University GRTF Program

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modelling and Simulation,Ecology, Evolution, Behavior and Systematics

Reference77 articles.

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4. Multiplex genome engineering using CRISPR/Cas systems;L Cong;Science,2013

5. Efficient genome editing in zebrafish using a CRISPR-Cas system;WY Hwang;Nat Biotechnol,2013

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